Artificial Neural Network Based Modeling of GaAs HBT and Power Amplifier Design for Wireless Communication System

RF imperfections can significantly degrade the performance of full-duplex wireless communication system by introducing non-idealities and random effects, which make it difficult to cancel the self-interference completely. In this paper, we first address the adverse benefits of both the transmitter non-linearity and the IQ imbalance. Then on the basis of these, a joint digital self-interference cancellation scheme is proposed, in which not only the effect of IQ imbalance and power amplifier non-linearity individually, but also the comprehensive function of them are taken into account. Furthermore, the simulation is implemented in the MATLAB platform using standard WiFi 802.11ac PHYs. The results show that the proposed canceller can eliminate more compared with other cancellation schemes, and the overall self-interference attenuation can attain 108dB, which makes the residual self-interference closer to the noise floor.

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Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network

IEEE Access ◽

10.1109/access.2020.3035209 ◽

2020 ◽

Vol 8 ◽

pp. 199523-199538 ◽

Cited By ~ 1

Author(s):

Lina Wu ◽

Danping He ◽

Bo Ai ◽

Jian Wang ◽

Hang Qi ◽

...

Keyword(s):

Neural Network ◽

Communication Network ◽

Artificial Neural Network ◽

Wireless Communication ◽

Path Loss ◽

Wireless Communication Network ◽

Loss Prediction ◽

Path Loss Prediction ◽

Artificial Neural

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Model and method for diagnosing computer incidents in information and communication systems based on deep machine learning

Information and Control Systems ◽

10.31799/1684-8853-2019-6-32-42 ◽

2019 ◽

pp. 32-42

Author(s):

Albert Malikov ◽

Vladimir Avramenko ◽

Igor Saenko

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Information Security ◽

Communication System ◽

Communication Systems ◽

Security Breach ◽

Main Requirement ◽

Information And Communication ◽

Artificial Neural ◽

Hidden Layer

Introduction: Models and methods for diagnosing computer incidents recorded in information and communication systems are the most important components in mathematical support of information security systems. The main requirement for the diagnostics is prompt identification of security violation characteristics. This problem is complicated due to the amount and variability of the initial data on information security violation. Purpose: Development of a model for diagnosing a computer incident, along with a method which would allow you to quickly determine the characteristics of a security violation. Results: Security breach characteristics important for making a decision about responding to an identified computer incident can be determined via deep artificial neural networks. A structural feature of the proposed deep artificial neural network is combining the coding part of the autoencoder and a multilayer perceptron. In addition, the method implements a parallel mode of processing information events which have occurred in the information and communication system before the incident was detected, by using a separate proposed artificial neural network for each secondary characteristic of the security breach. The method of determining the values of these secondary characteristics allows you to greatly improve the diagnostics efficiency, having acceptable values of precision and recall for the security violation characteristics to determine. The dependence has been studied of the completeness and classification accuracy on the number of neurons in the hidden layer. A sufficient number of neurons in the hidden layer for achieving the required training efficiency is experimentally determined. Practical relevance: The developed model and method can be implemented using standard software and hardware (servers) of an information and communication system. Their combined use with the existing models and methods of monitoring and diagnostics can significantly improve the efficiency of an information security system.

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